When Machines Speak Science: Testing Consumers’ Perceptions of AI-Generated Communication Messages - Alexa J. Lamm; Kevan W. Lamm; Allison R Byrd; Masoud Yazdanpanah; Nicholas K. Gabler; Anna K. Johnson; Catherine Sanders; Fallys Masambuka-Kanchewa; Michael S. Retallick (Journal of Applied Communications, 2025)
Effective science communication is essential for fostering public understanding and trust in scientific information, particularly as emerging technologies like artificial intelligence (AI) are used to generate content quickly. The purpose of this research was to explore how U.S. consumers perceive transparency and source credibility of an agricultural science communication message delivered via Facebook when messages are generated by AI versus a human scientist. Using an experimental design embedded in an online survey, 1,011 respondents, representative of the U.S. public, were randomly assigned to view one of four social media posts covering either a benign (animal nutrition) or controversial (animal welfare) topic, authored by AI or a scientist. Respondents rated the perceived transparency of the messages before and after the source was revealed. Results indicated perceived transparency significantly decreased when respondents discovered the post was AI-generated and increased when they learned it was scientist-generated. Topic contentiousness did not have a significant influence. Findings suggest a preference for human-generated science communication messages, emphasizing the importance of source disclosure and highlighting challenges to public acceptance of AI-generated messaging. Science communicators should be cautious using AI when communicating science to the public until trust and acceptance of AI improves.
Keywords: artificial intelligence; consumer perception; science communication